Introducing locally affine-invariance constraints into lunar surface image correspondence
Zhang, Yu-Ren1; Yang, Xu1; Qiao, Hong1,3; Liu, Zhi-Yong1; Liu, Chuan-Kai2
Source PublicationNEUROCOMPUTING
2016-04-19
Volume186Issue:Pages:258-270
SubtypeArticle
AbstractThis paper aims to solve the keypoint correspondence problem in lunar surface images, a typical correspondence task under point ambiguity. Point ambiguity may be caused by repetitive patterns, cluttered scenes, and outliers in the images, which makes the local descriptors less discriminative. In this paper we introduce locally affine-invariance constraints on graphs to tackle the keypoint correspondence problem under point ambiguity. The key idea is that each point can be represented with the affine combination of its neighbors. It is suitable for our problem because it is not only invariant to scale and rotational change, but also more resistant to outliers. Specifically, we introduce the locally affine-invariance constraints into the subgraph matching problem and the common subgraph matching problem. The locally affine-invariance constraint is not directly applicable on common subgraph matching due to its dependency on awareness of selected keypoints. This problem is approximately addressed by solving a series of reliable matching identification and rematching problems. In the experiments, we first apply the proposed method on standard graph matching datasets to evaluate its effectiveness on general correspondence problem under point ambiguity, and second validate the applicability on the lunar surface image dataset. (C) 2016 Elsevier B.V. All rights reserved.
KeywordKeypoint Correspondence Point Ambiguity Graph Matching Lunar Image Processing
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.neucom.2015.12.082
WOS KeywordROBUST ; GNCCP
Indexed BySCI
Language英语
Funding OrganizationNational Science Foundation of China (NSFC)(61375005 ; Beijing Municipal Science & Technology Commission(2141100002014002) ; National Key Technology RD Program(2012BAI34B02) ; 61033011 ; 61210009 ; 61101221)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000374366300025
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11629
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorYang, Xu
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
2.Beijing Aerosp Flight Control Ctr, Beijing, Peoples R China
3.Chinese Acad Sci, CEBSIT, Shanghai, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Yu-Ren,Yang, Xu,Qiao, Hong,et al. Introducing locally affine-invariance constraints into lunar surface image correspondence[J]. NEUROCOMPUTING,2016,186(无):258-270.
APA Zhang, Yu-Ren,Yang, Xu,Qiao, Hong,Liu, Zhi-Yong,&Liu, Chuan-Kai.(2016).Introducing locally affine-invariance constraints into lunar surface image correspondence.NEUROCOMPUTING,186(无),258-270.
MLA Zhang, Yu-Ren,et al."Introducing locally affine-invariance constraints into lunar surface image correspondence".NEUROCOMPUTING 186.无(2016):258-270.
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